Fetching the paper…
Reading the bibliography…
Neural implicit representations, including Neural Distance Fields and Neural Radiance Fields, have demonstrated significant capabilities for reconstructing surfaces with complicated geometry and topology, and generating novel views of a scene.
S. Ullman, “The interpretation of structure from motion,”
1979
Earlier work this paper cites.
J. T. Kajiya and B. P. Von Herzen, “Ray tracing volume densities,”
1984
Earlier work this paper cites.
J. Bonet and R. D. Wood,
1997
Earlier work this paper cites.
M. Zwicker, H. Pfister, J. Van Baar, and M. Gross, “Surface splatting,” in
2001
Earlier work this paper cites.
Y. Yu, K. Zhou, D. Xu, X. Shi, H. Bao, B. Guo, and H.-Y. Shum, “Mesh editing with Poisson-based gradient field manipulation,” in
2004
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,”
2004
Earlier work this paper cites.
Y. Lipman, O. Sorkine-Hornung, M. Alexa, D. Cohen-Or, D. Levin, C. Rössl, and H.-P. Seidel, “Laplacian framework for interactive mesh editing,”
2005
Earlier work this paper cites.
O. Sorkine-Hornung, “Laplacian mesh processing,” in
2005
Earlier work this paper cites.
R. W. Sumner, M. Zwicker, C. Gotsman, and J. Popović, “Mesh-based inverse kinematics,”
2005
Earlier work this paper cites.
O. Sorkine and M. Alexa, “As-rigid-as-possible surface modeling,” in
2007
Earlier work this paper cites.
C. Pinson, “Sketchfab - the best 3d viewer on the web,” https://sketchfab.com/, 2011
2011
Earlier work this paper cites.
D. Maturana and S. Scherer, “VoxNet: A 3D convolutional neural network for real-time object recognition,” in
2015
Earlier work this paper cites.
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese, “3D-R2N2: A unified approach for single and multi-view 3D object reconstruction,” in
2016
Earlier work this paper cites.
J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum, “Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling,” in
2016
Earlier work this paper cites.
J. L. Schönberger and J.-M. Frahm, “Structure-from-motion revisited,” in
2016
Earlier work this paper cites.
J. L. Schönberger, E. Zheng, M. Pollefeys, and J.-M. Frahm, “Pixelwise view selection for unstructured multi-view stereo,” in
2016
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep learning on point sets for 3D classification and segmentation,” in
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “PointNet++: Deep hierarchical feature learning on point sets in a metric space,” in
2017
Earlier work this paper cites.
H. Fan, H. Su, and L. J. Guibas, “A point set generation network for 3D object reconstruction from a single image,” in
2017
Earlier work this paper cites.
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3D point clouds,” in
2018
Earlier work this paper cites.
N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, and Y.-G. Jiang, “Pixel2mesh: Generating 3D mesh models from single RGB images,” in
2018
Earlier work this paper cites.
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry, “AtlasNet: A papier-mâché approach to learning 3D surface generation,” in
2018
Earlier work this paper cites.
P.-S. Wang, C.-Y. Sun, Y. Liu, and X. Tong, “Adaptive o-cnn: A patch-based deep representation of 3d shapes,”
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” 2018, pp. 586–595
2018
Earlier work this paper cites.
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, “Occupancy networks: Learning 3D reconstruction in function space,” in
2019
Earlier work this paper cites.
Z. Chen and H. Zhang, “Learning implicit fields for generative shape modeling,” in
2019
Earlier work this paper cites.
L. Gao, Y.-K. Lai, J. Yang, L.-X. Zhang, S. Xia, and L. Kobbelt, “Sparse data driven mesh deformation,”
2019
Cited alongside, same era.
R. Hanocka, A. Hertz, N. Fish, R. Giryes, S. Fleishman, and D. Cohen-Or, “Meshcnn: a network with an edge,”
2019
Cited alongside, same era.
L. Gao, J. Yang, T. Wu, Y.-J. Yuan, H. Fu, Y.-K. Lai, and H. Zhang, “SDM-NET: Deep generative network for structured deformable mesh,”
2019
Cited alongside, same era.
J. Chibane, T. Alldieck, and G. Pons-Moll, “Implicit functions in feature space for 3d shape reconstruction and completion,” in
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Wang, Q. Han, M. Habermann, K. Daniilidis, C. Theobalt, and L. Liu, “Neus2: Fast learning of neural implicit surfaces for multi-view reconstruction,” in
2023
Later among the works it cites.
Y.-T. Liu, L. Wang, J. Yang, W. Chen, X. Meng, B. Yang, and L. Gao, “Neudf: Leaning neural unsigned distance fields with volume rendering,” in
2023
Later among the works it cites.
Z. Li, T. Müller, A. Evans, R. H. Taylor, M. Unberath, M.-Y. Liu, and C.-H. Lin, “Neuralangelo: High-fidelity neural surface reconstruction,” in
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Yifan, N. Aigerman, V. G. Kim, S. Chaudhuri, and O. Sorkine-Hornung, “Neural cages for detail-preserving 3D deformations,” in
2020
Cited alongside, same era.
Y. Zhang, J. Zheng, and Y. Cai, “Proxy-driven free-form deformation by topology-adjustable control lattice,”
2020
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,”
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
S. Liu, X. Zhang, Z. Zhang, R. Zhang, J.-Y. Zhu, and B. Russell, “Editing conditional radiance fields,” in
2021
Cited alongside, same era.
A. Yu, R. Li, M. Tancik, H. Li, R. Ng, and A. Kanazawa, “Plenoctrees for real-time rendering of neural radiance fields,” in
2021
Cited alongside, same era.
2023
Later among the works it cites.
R. Liu, R. Wu, B. Van Hoorick, P. Tokmakov, S. Zakharov, and C. Vondrick, “Zero-1-to-3: Zero-shot one image to 3d object,” in
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Wang, J. Zhu, Q. Ye, Y. Huo, Y. Ran, Z. Zhong, and J. Chen, “Seal-3d: Interactive pixel-level editing for neural radiance fields,” in
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Kocabas, J.-H. R. Chang, J. Gabriel, O. Tuzel, and A. Ranjan, “Hugs: Human gaussian splats,”
2023
Later among the works it cites.
Z. Chen, F. Wang, and H. Liu, “Text-to-3d using gaussian splatting,”
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Wang, R. Jiang, M. Chai, M. He, D. Chen, and J. Liao, “Nerf-art: Text-driven neural radiance fields stylization,”
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Bao, Y. Zhang, and B. e. a. Yang, “Sine: Semantic-driven image-based nerf editing with prior-guided editing field,” in
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Jambon, B. Kerbl, G. Kopanas, S. Diolatzis, G. Drettakis, and T. Leimkühler, “Nerfshop: Interactive editing of neural radiance fields,”
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Jiang, Z. Ke, X. Zhou, and X. Shi, “4d-editor: Interactive object-level editing in dynamic neural radiance fields via semantic distillation,”
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Su, T. Yu, Y. Wang, and Y. Liu, “Deepcloth: Neural garment representation for shape and style editing,”
2023
Later among the works it cites.
G. Chen and W. Wang, “A survey on 3d gaussian splatting,”
2024
Closest in time.
J. Luiten, G. Kopanas, B. Leibe, and D. Ramanan, “Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis,” in
2024
Closest in time.